Python's NumPy Library

I. Overview of NumPy

NumPy (numerical python) provides Python support for multidimensional array objects: Ndarray, with vector computing power, fast and space-saving. NumPy supports advanced large number of dimension and matrix operations, and also provides a large number of mathematical libraries for array operations.

Ii. creating an array of Ndarray

ndarray:n-dimensional array objects (matrices), all elements must be of the same type.
Ndarray Property: The Ndim property, which represents the number of dimensions; the Shape property, which represents the size of each dimension; The Dtype property that represents the data type.

To create an Ndarray array function:

Iii. specifying the type of the Ndarray array element

NumPy Data type:

Vectorization Calculation of Ndarray

Vector operations: An operation between an array key of the same size applied to an element
Vector and scalar operations: "Broadcast"-scalar "broadcast" to individual elements

V. Basic indexes and slices of the Ndarray array

Index of one-dimensional array: Similar to Python's list index function

Index of multidimensional arrays:

• ARR[R1:R2, C1:C2]
• arr[1,1] equivalent arr[1][1]
• [:] represents data for a dimension

Vi. Boolean indexes and fancy indexes for ndarray arrays

Boolean index: Uses a Boolean array as the index. Arr[condition],condition is a Boolean array that consists of one condition/multiple conditions.

Fancy index: Use an integer array as the index.

The transpose of the Ndarray array and the axis swap

The transpose/pivot of an array returns only one view of the source data and does not modify the source data.

Viii. General functions of Ndarray

A general function (UFUNC) is a function that performs an element-level operation on data in Ndarray.

One dollar Ufunc:

Dual Ufunc:

Nine, the WHERE function of NumPy uses

Np.where (condition, x, y), the first parameter is a Boolean array, the second argument and the third parameter can be scalar or an array.

X. Statistical methods commonly used in Ndarray

The data of an entire array/axis can be statistically calculated by these basic statistical methods.

Statistical methods for Boolean arrays:

• Sum: Counts the number of true in a dimension of an array/array
• Any: Statistics array/Array if there is one/more true in a dimension
• All: counts whether the array/array is true in one dimension

Use sort to sort the array/array in-place with a dimension (the arrays themselves are modified).

The de-weight of the Ndarray array and the set operation

12. Linear Algebra in NumPy

Import Numpy.linalg module. Linear algebra (linear algebra)

Common NUMPY.LINALG Module functions:

13. Generation of random numbers in NumPy

Import Numpy.random module.

Common Numpy.random Module functions:

Python's NumPy Library